Various groups of heart rhythm disorders are known, some of which are life-threatening and require immediate attention and treatment, such as ventricular fibrillation and ventricular tachycardia, and others which may require treatment but not as immediately, such as atrial fibrillation. Atrial fibrillation (“AF”), for example, is a common rhythm disturbance of the heart associated with increased risk of stroke and death. Ventricular fibrillation is much less common, but it always results in death within minutes, unless it is converted to a less dangerous rhythm. Ventricular tachycardia is also not common, but may result in death if not treated promptly.
Currently, AF is diagnosed by symptoms or is discovered incidentally. Available evidence indicates that a significant portion of patients with AF do not have symptoms, and consequently may not be discovered during routine medical examinations unless they happen to be in atrial fibrillation at the time of the examination. However, AF may be diagnosed using medical equipment, such as rhythm monitors. Monitoring techniques used by available rhythm monitors include monitoring the heart rhythm for a short period of time or intermittently. Unfortunately, these monitoring techniques have low sensitivity for the detection of AF if it is not present during the short monitoring period. Conventional rhythm monitors have limited storage capacity for storing monitoring data used to determine the extent of AF.
AF is the most common disturbance of the heart rhythm requiring treatment. Epidemiologic data estimates that 2.2 million individuals suffer from AF in the United States. The incidence of AF increases with age. The prevalence of AF is approximately 2-3% in patients older than 40 years of age and 6% in those individuals over 65 years and 9% in individuals over 80 years old. Feinberg, W. M., et al., Prevalence, Age Distribution, and Gender of Patients With Atrial Fibrillation, 155 Arch. Intern. Med. 469 (1995). As the U.S. population ages, AF will become more prevalent. It is estimated that over 5 million Americans will suffer from AF by the year 2050. Go, A. S., et al., Prevalence of Diagnosed Atrial Fibrillation in Adults: National Implications for Rhythm Management and Stroke Prevention: the Anticoagulation and Risk Factors in Atrial Fibrillation (ATRIA) Study, 285 JAMA, 2370 (2001). AF is associated with a doubling of mortality rate of people afflicted with AF compared to people who are not, and an increased risk of stroke of about 5% per year. Fuster, V., et al., ACC/AHA/ESC Guidelines for the Management of Patients With Atrial Fibrillation, 22 Eur. Heart J. 1852 (October 2001).
AF can be either symptomatic or asymptomatic, and can be paroxysmal or persistent. Symptomatic AF is a medical condition wherein symptoms are readily detectable by experts in the field. AF is usually diagnosed when a patient reveals associated symptoms or complications, such as congestive heart failure or stroke. AF may also be diagnosed incidentally during a routine medical evaluation. Asymptomatic AF is a medical condition wherein symptoms normally associated with AF are either absent or are not readily detectable by experts in the field.
Paroxysmal AF comprises occasional episodes of the AF condition in the patient. Persistent AF is a continuous existence of the condition. Patients with asymptomatic paroxysmal AF may be exposed to the risk of devastating consequences such as stroke, congestive heart failure, or tachycardia mediated cardiomyopathy, for years before a definitive diagnosis of AF can be made.
Current standard techniques and devices for detecting AF include a resting electrocardiogram, which records about 15 seconds of cardiac activity, a Holter monitor, which records 24-48 hours of cardiac activity during routine daily activities, and an event monitor, which only records cardiac activity when the patient activates the monitor because the patient has detected symptoms associated with AF. These diagnostic methods and tools have significant limitations in diagnosing AF and assessing the efficacy of treatment because of the limited recording time windows of these methods and tools. The prevalence of asymptomatic AF is difficult to assess, but is clearly underrepresented in the figures quoted above.
Pharmacologic treatment of AF may convert patients with symptomatic AF into patients with asymptomatic AF. In a retrospective study of four studies comparing Azimilide drug to placebo where, in the absence of symptoms, routine trans-telephonic electrocardiograms were recorded for 30 seconds every two weeks, asymptomatic AF was present in 17% of the patients. Page, R. L., et al., Asymptomatic or “Silent” Atrial Fibrillation: Frequency in Untreated Patients and Patients Receiving Azimilide, 107 Circulation 1141 (2003).
In another study of 110 patients with permanently implanted pacemakers who had a history of AF, the condition was diagnosed in 46% of the patients using electrocardiogram (“EKG”) recording and in 88% of the patients using stored electrograms recorded by the implanted pacemaker. Israel, C. W., et al., Long-Term Risk of Recurrent Atrial Fibrillation as Documented by an Implantable Monitoring Device, 43 J. Am. Coll. Cardiol. 47 (2004).
Review of data stored in implanted devices, such as pacemakers, revealed that 38% of AF recurrences lasting greater than 48 hours were completely asymptomatic. Finally, using data obtained from ambulatory monitors used on patients with paroxysmal AF over a 24-hour period, studies show a high frequency of occurrence of asymptomatic AF among patients treated with propranolol or propafenone drugs. Wolk, R., et al., The Incidence of Asymptomatic Paroxysmal Atrial Fibrillation in Patients Treated With Propranolol or Propafenone, 54 Int. J. Cardiol. 207 (1996). In the above-mentioned study, 22% of the patients on propranolol and 27% of the patients on propafenone were diagnosed with AF without symptoms.
There is also evidence that previously undetected AF is associated with stroke. About 4% of patients with stroke admitted to a medical facility also had newly diagnosed AF which was thought to be a precipitating cause of the stroke. Lin, H. J., et al., Newly Diagnosed Atrial Fibrillation and Acute Stroke, The Framingham Study, 26 Stroke 1527 (1995).
Under-detection and under-recognition of AF in patients may have significant clinical consequences. A first consequence includes clinical exposure of patients to a significant risk of cardioembolic stroke before detection of the arrhythmia and initiation of appropriate stroke prevention measures.
A second consequence includes difficulty of assessment of the efficacy of rhythm control intervention. Physicians caring for such patients may erroneously conclude that AF is no longer present and inappropriately discontinue anticoagulation treatments which may lead to a devastating cardioembolic stroke. Consequently, once diagnosed with AF, many patients may be committed to life-long anticoagulation by the physician to avoid the latter issues.
A third consequence includes overestimation of successful maintenance of sinus rhythm. Clinical studies evaluating the efficacy of various rhythm control strategies may overestimate the successful maintenance of sinus rhythm as many of these studies report symptomatic AF as an endpoint. An accurate long term monitoring device would enhance the diagnostic yield of capturing asymptomatic paroxysmal atrial fibrillation, potentially allowing the safe withdrawal of anticoagulation treatments in patients treated successfully with anti-arrhythmic agents, identifying the patients at risk who are currently not diagnosed as having AF, and providing a more precise measure of the efficacy of pharmacologic and non-pharmacologic rhythm control strategies.
Detection of AF, automatically or manually, based on statistical data, requires the use of thresholds defined with respect to sensitivity and specificity. The thresholds used define the point beyond which a set of data indicate existence of AF. Sensitivity and specificity are defined as follows. In a dichotomous experiment, a given event, e, falls into one of two sets, such as a set of positive events, P, and a set of negative events, N. The set P includes events p and the set N includes events n.
A detection test may be performed to determine that the given event e belongs to the set P or to the set N in a dichotomous experiment. Sensitivity is a measure of how well the detection test can correctly identify the given event e of the set P as belonging to the set P. Such events e that are correctly identified as belonging to the set P are known as true positives (“TP”). Such events e that are misidentified as belonging to the set N are known as false negatives (“FN”).
Sensitivity is defined as the ratio of the number of true positive events detected correctly by the test to the total number of actual positive events p. The total number of actual positive events is equal to the sum of the TP and FN. That is, sensitivity=TP/(TP+FN). A low sensitivity detection test will misidentify more positive events as belonging to the set N than a high sensitivity detection test.
Specificity is the dual of sensitivity and is a measure of how well the detection test can correctly identify the given event e of the set N as belonging to the set N. Such events e that are correctly identified as belonging to the set N are known as true negatives (“TN”). Such events e that are misidentified as belonging to the set P are known as false positives (“FP”). Specificity is defined as the ratio of the number of true negative events detected correctly by the test to the total number of actual negative events n. The total number of actual negative events is equal to the sum of the TN and FP. That is, specificity=TN/(TN+FP). A low specificity detection test will misidentify more negative events as belonging to the set P than a high specificity detection test.
A number of techniques have been used for the automated detection of AF from digitized electrocardiograms. One of the techniques used includes the use of intracardiac recordings obtained from implanted devices showing a sensitivity of close to 100% and a specificity of greater than 99%. Swerdlow, C. D., et al., Detection of Atrial Fibrillation and Flutter by a Dual-Chamber Implantable Cardioverter-Defibrillator, 101 Circulation 878 (2000).
A method for analysis of the surface monitor leads using a wavelet transform achieved a sensitivity of 96% and specificity of 93% in recordings from patients with paroxysmal atrial fibrillation. Duverney, D. et al., High Accuracy of Automatic Detection of Atrial Fibrillation Using Wavelet Transform of Heart Rate Intervals, 25(4) Pacing and Clinical Electrophysiology 457 (2002). At least one group has proposed using wavelets for implantable/wearable monitoring devices. Ang, N. H., Real-Time Electrocardiogram (ECG) Signal Processing for Atrial Fibrillation (AF) Detection, Modeling Seminar-Archive (2003).
A prominent characteristic of AF is heart rate variability. There have been attempts to use the variability of heart interbeat (“RR”) intervals directly to identify AF, resulting in a sensitivity of 94% and specificity of 97% using a threshold based on the Kolmogorov-Smirnov test. Tateno, K. and Glass, L., Automatic Detection of Atrial Fibrillation Using the Coefficient of Variation and Density Histograms of RR and ΔRR Intervals, 39(6) Med. Biol. Eng. Comput. 664 (2001). The Kolmogorov-Smirnov test (Chakravart, Laha, and Roy, 1967) is used to decide if a statistical sample belongs to a population with a specific probability distribution.
Long-term monitoring of cardiac activity is desirable for timely detection of AF, but the storage requirements can be prohibitive. To digitize a single channel EKG at 100 samples per second and 10-bit resolution, which constitute near minimum requirements for a high quality signal, for 90 days of continuous recording requires 927 megabytes of storage. Although providing this amount of storage is possible, it is also costly. Advances in electronics allow the design of portable devices that can pre-process and classify the signals to avoid storage of normal rhythms and save the storage capacity for recording of abnormal rhythms indicating existence of atrial fibrillation.
Selective storage of signals that potentially indicate AF as opposed to normal heart rhythm effectively increases the storage capacity and prolongs the recording period. At least two such devices exist in the market. One such device has been developed by Instromedix (San Diego, Calif.), and is available in two versions. Each version can monitor the heart rhythm for up to 30 days, capturing a total of 10 minutes of potentially abnormal EKG. The device weighs about 4 ounces. The other device is based on satellite telephone technology, and transmits the suspect rhythms to a monitoring facility. For such a device, accurate algorithms are also important, since high sensitivity will result in a high probability of detection, and high specificity will avoid transmission and review of normal rhythms.
Recently, another device was announced with a detection rate of 90% and a monitoring storage capacity equivalent to 60 minutes of recorded data. A device for home use which does “momentary” analysis of the electrocardiogram as the patient grasps handles on the device daily is disclosed in U.S. Pat. No. 6,701,183, issued to Baker et al. on Mar. 2, 2004, entitled Long Term Atrial Fibrillation Monitor. 
A device is desired for the long-term monitoring of AF that is inexpensive, non-invasive, highly accurate, and convenient for the patient. These requirements at least indicate that the monitoring device should be light and small. As such, a device is desired with low power requirements and with a significant amount of storage. The storage capacity may possibly be extended by using an algorithm for the elimination of EKG data that indicate very low-probability of AF. This algorithm should be small in size and simple in operation to reduce processing power needs and electrical power requirements. The existing algorithms based on wavelets appear to be overly complex for this type of application, requiring a significant amount of processing and electrical power as well as storage capacity.
As should be apparent to one skilled in the art, there are situations where the accurate detection of AF would be desirable in an implantable device as well. There are implantable devices that are intended solely for diagnosis of rhythm disturbances, and devices implanted for therapy which have additional diagnostic functions. These devices would also benefit from accurate, low computational complexity detection of AF. It should also be apparent that devices intended to treat AF, either by electric shocks delivered to the heart, or medications administered to control atrial fibrillation, such as propafenone, amiodarone or beta-blockers, or to convert AF, such as ibutilide, would benefit from accurate, low-computational cost complexity detection of AF.
For response to and treatment of ventricular tachycardia and ventricular fibrillation in real-time, often an implantable cardiac defibrillator (“ICD”) is surgically implanted in the patient and coupled with the heart to monitor heart rhythm and detect these life-threatening rhythm disturbances. The ICD typically includes logic components implemented in software/firmware and/or hardware for detecting arrhythmia. Once life-threatening arrhythmia is detected, the ICD logic component may, based on a discrimination algorithm, determine that some action, such as administering an electric shock (defibrillation), must be taken to treat the arrhythmia.
However, this determination can be erroneous. In some ICDs such inappropriate shocks can occur in 15% of all patients within a 46-month follow-up. Alter, P., et al., Complications of Implantable Cardioverter Defibrillator Therapy in 440 Consecutive Patients, 28(9) Pacing and Clinical Electrophysiology, 926 (2005). Certain sub-populations may have a higher rate of inappropriate shocks, for example, this can occur in as many as 38% of younger patients. Costa, R., et al., Incidence of Shock and Quality of Life in Young Patients with Implantable Cardioverter-Defibrillator, 88(3) Arq. Bras. Cardiol. 258 (2007). A common cause of inappropriate shock in these patients is AF, although virtually any supraventricular tachycardia can cause an inappropriate shock.
When not needed, an electric shock causes extreme discomfort and/or pain to the patient and may be potentially dangerous. Accordingly, a more accurate discrimination algorithm is needed to discriminate between cases where an electric shock is needed and cases where an electric shock is not needed. One approach that has been used is based on the heart rate, or beat-to-beat intervals, as in a recent study by Mletzko. Mletzko, R., et al., Enhanced Specificity of a Dual Chamber ICD Arrhythmia Detection Algorithm by Rate Stability Criteria, 27(8) Pacing and Clinical Electrophysiology 1113 (2004).
Another approach uses the morphology of the electrocardiographic complexes to help discriminate, with a representative recent method being the use of wavelet-transforms. Klein, G. J., et al., Improving SVT Discrimination in Single-Chamber ICDs: A New Electrogram Morphology-Based Algorithm, 17(12) J. Cardiovascular Electrophysiology 1310 (2006). In spite of these methods, inappropriate shocks continue to be a problem in such devices.
An additional constraint is that, since the devices are battery powered and implanted, power consumption is a major consideration. Cebrián, A., et al., Implantable Cardioverter Defibrillator Algorithms: Status Review in Terms of Computational Cost, 52(1) Biomed. Tech. (Berl) 25 (2007).
The power consumption is related to the complexity of the algorithm, and the hardware required. The ideal algorithm would not require specialized hardware, and would have a low computational complexity, so that power consumption would be low.